title: “Weight lifting analysis using R” author: “Harsh Gambhir”
This is a work i have produced for my data science specialization sub course and the analysis is about working on different technique
abc <- read.csv("pml-training.csv", stringsAsFactors = F,na.strings = c("","NA","#DIV/0!"))
def <- read.csv("pml-testing.csv", stringsAsFactors = F,na.strings = c("","NA","#DIV/0!"))
dim(abc); dim(def)## [1] 19622 160## [1] 20 160set.seed(101)
jkl <- createDataPartition(abc$classe, p = 0.8, list = F)
ghi <- abc[-jkl,]
abc <- abc[jkl,]
dim(abc); dim(ghi)## [1] 15699 160## [1] 3923 160table(abc$classe)/nrow(abc)##
## A B C D E
## 0.2843493 0.1935155 0.1744060 0.1638958 0.1838334## roll_belt pitch_belt yaw_belt
## 0 0 0
## total_accel_belt kurtosis_roll_belt kurtosis_picth_belt
## 0 15396 15413
## kurtosis_yaw_belt skewness_roll_belt skewness_roll_belt.1
## 15699 15395 15413
## skewness_yaw_belt max_roll_belt max_picth_belt
## 15699 15388 15388
## max_yaw_belt min_roll_belt min_pitch_belt
## 15396 15388 15388
## min_yaw_belt amplitude_roll_belt amplitude_pitch_belt
## 15396 15388 15388
## amplitude_yaw_belt var_total_accel_belt avg_roll_belt
## 15396 15388 15388
## stddev_roll_belt var_roll_belt avg_pitch_belt
## 15388 15388 15388
## stddev_pitch_belt var_pitch_belt avg_yaw_belt
## 15388 15388 15388
## stddev_yaw_belt var_yaw_belt gyros_belt_x
## 15388 15388 0
## gyros_belt_y gyros_belt_z accel_belt_x
## 0 0 0
## accel_belt_y accel_belt_z magnet_belt_x
## 0 0 0
## magnet_belt_y magnet_belt_z
## 0 0pqr <- sapply(select(abc,names(abc)[grepl("_arm",names(abc))]),
function(x) sum(is.na(x)))
pqr## roll_arm pitch_arm yaw_arm total_accel_arm
## 0 0 0 0
## var_accel_arm avg_roll_arm stddev_roll_arm var_roll_arm
## 15388 15388 15388 15388
## avg_pitch_arm stddev_pitch_arm var_pitch_arm avg_yaw_arm
## 15388 15388 15388 15388
## stddev_yaw_arm var_yaw_arm gyros_arm_x gyros_arm_y
## 15388 15388 0 0
## gyros_arm_z accel_arm_x accel_arm_y accel_arm_z
## 0 0 0 0
## magnet_arm_x magnet_arm_y magnet_arm_z kurtosis_roll_arm
## 0 0 0 15446
## kurtosis_picth_arm kurtosis_yaw_arm skewness_roll_arm skewness_pitch_arm
## 15448 15398 15445 15448
## skewness_yaw_arm max_roll_arm max_picth_arm max_yaw_arm
## 15398 15388 15388 15388
## min_roll_arm min_pitch_arm min_yaw_arm amplitude_roll_arm
## 15388 15388 15388 15388
## amplitude_pitch_arm amplitude_yaw_arm
## 15388 15388forepqr <- sapply(select(abc,
names(abc)[grepl("_forearm",names(abc))]),
function(x) sum(is.na(x)))
forepqr## roll_forearm pitch_forearm yaw_forearm
## 0 0 0
## kurtosis_roll_forearm kurtosis_picth_forearm kurtosis_yaw_forearm
## 15448 15449 15699
## skewness_roll_forearm skewness_pitch_forearm skewness_yaw_forearm
## 15447 15449 15699
## max_roll_forearm max_picth_forearm max_yaw_forearm
## 15388 15388 15448
## min_roll_forearm min_pitch_forearm min_yaw_forearm
## 15388 15388 15448
## amplitude_roll_forearm amplitude_pitch_forearm amplitude_yaw_forearm
## 15388 15388 15448
## total_accel_forearm var_accel_forearm avg_roll_forearm
## 0 15388 15388
## stddev_roll_forearm var_roll_forearm avg_pitch_forearm
## 15388 15388 15388
## stddev_pitch_forearm var_pitch_forearm avg_yaw_forearm
## 15388 15388 15388
## stddev_yaw_forearm var_yaw_forearm gyros_forearm_x
## 15388 15388 0
## gyros_forearm_y gyros_forearm_z accel_forearm_x
## 0 0 0
## accel_forearm_y accel_forearm_z magnet_forearm_x
## 0 0 0
## magnet_forearm_y magnet_forearm_z
## 0 0stu <- sapply(select(abc,
names(abc)[grepl("_dumbbell",names(abc))]),
function(x) sum(is.na(x)))
stu## roll_dumbbell pitch_dumbbell yaw_dumbbell
## 0 0 0
## kurtosis_roll_dumbbell kurtosis_picth_dumbbell kurtosis_yaw_dumbbell
## 15392 15390 15699
## skewness_roll_dumbbell skewness_pitch_dumbbell skewness_yaw_dumbbell
## 15391 15389 15699
## max_roll_dumbbell max_picth_dumbbell max_yaw_dumbbell
## 15388 15388 15392
## min_roll_dumbbell min_pitch_dumbbell min_yaw_dumbbell
## 15388 15388 15392
## amplitude_roll_dumbbell amplitude_pitch_dumbbell amplitude_yaw_dumbbell
## 15388 15388 15392
## total_accel_dumbbell var_accel_dumbbell avg_roll_dumbbell
## 0 15388 15388
## stddev_roll_dumbbell var_roll_dumbbell avg_pitch_dumbbell
## 15388 15388 15388
## stddev_pitch_dumbbell var_pitch_dumbbell avg_yaw_dumbbell
## 15388 15388 15388
## stddev_yaw_dumbbell var_yaw_dumbbell gyros_dumbbell_x
## 15388 15388 0
## gyros_dumbbell_y gyros_dumbbell_z accel_dumbbell_x
## 0 0 0
## accel_dumbbell_y accel_dumbbell_z magnet_dumbbell_x
## 0 0 0
## magnet_dumbbell_y magnet_dumbbell_z
## 0 0column_2drop <- c(names(mno[mno != 0]),
names(pqr[pqr != 0]),
names(forepqr[forepqr != 0]),
names(stu[stu != 0]))
length(column_2drop)## [1] 100vwx <- tibble::as_tibble(abc %>%
select(-column_2drop,
-c(X,user_name, raw_timestamp_part_1,
raw_timestamp_part_2, cvtd_timestamp,
new_window,num_window)))## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(column_2drop)` instead of `column_2drop` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.vwx$classe <- as.factor(vwx$classe)
vwx[,1:52] <- lapply(vwx[,1:52],as.numeric)
dim(vwx)## [1] 15699 53corr_col <- cor(select(vwx, -classe))
diag(corr_col) <- 0
corr_col <- which(abs(corr_col)>0.8,arr.ind = T)
corr_col <- unique(row.names(corr_col))
corrplot(cor(select(vwx,corr_col)),
type="upper", order="hclust",method = "number")## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(corr_col)` instead of `corr_col` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
corr_funl_df <- vwx %>% binarize(n_bins = 4, thresh_infreq = 0.01)corr_a <- corr_funl_df %>% correlate(target = classe__A)
corr_a %>% plot_correlation_funnel(interactive = T,limits = c(-0.5,0.5))corr_b <- corr_funl_df %>% correlate(target = classe__B)
corr_b %>% plot_correlation_funnel(interactive = T,limits = c(-0.5,0.5))corr_c <- corr_funl_df %>% correlate(target = classe__C)
corr_c %>% plot_correlation_funnel(interactive = T,limits = c(-0.5,0.5))corr_d <- corr_funl_df %>% correlate(target = classe__D)
corr_d %>% plot_correlation_funnel(interactive = T,limits = c(-0.5,0.5))corr_e <- corr_funl_df %>% correlate(target = classe__E)
corr_e %>% plot_correlation_funnel(interactive = T,limits = c(-0.5,0.5))col_a <- c("magnet_arm_x", "pitch_forearm" , "magnet_dumbbell_y",
"roll_forearm", "gyros_dumbbell_y")
col_b <- c("magnet_dumbbell_y", "magnet_dumbbell_x" , "roll_dumbbell" ,
"magnet_belt_y" , "accel_dumbbell_x" )
col_c <- c("magnet_dumbbell_y", "roll_dumbbell" , "accel_dumbbell_y" ,
"magnet_dumbbell_x", "magnet_dumbbell_z")
col_d <- c("pitch_forearm" , "magnet_arm_y" , "magnet_forearm_x",
"accel_dumbbell_y", "accel_forearm_x")
col_e <- c("magnet_belt_y" , "magnet_belt_z" , "roll_belt",
"gyros_belt_z" , "magnet_dumbbell_y")
final_cols <- character()
for(c in c(col_a,col_b,col_c,col_d,col_e)){
final_cols <- union(final_cols, c)
}
vwx2 <- vwx %>% select(final_cols, classe)## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(final_cols)` instead of `final_cols` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.data.frame("arm" = sum(grepl("_arm",final_cols)),
"forearm" = sum(grepl("_forearm",final_cols)),
"belt" = sum(grepl("_belt",final_cols)),
"dumbbell" = sum(grepl("_dumbbell",final_cols)))## arm forearm belt dumbbell
## 1 2 4 4 7my_dens <- function(data, mapping, ...) {
ggplot(data = data, mapping=mapping) +
geom_density(..., alpha = 0.3)+scale_fill_brewer(palette="Set2")
}
my_point <- function(data, mapping, ...) {
ggplot(data = data, mapping=mapping) +
geom_point(..., alpha = 0.1)+ scale_fill_brewer(palette="Set2")
}
ggpairs(vwx2, columns = 1:5,aes(color = classe),
lower = list(continuous = my_point),diag = list(continuous = my_dens))
ggpairs(vwx2, columns = 6:10,aes(color = classe),
lower = list(continuous = my_point),diag = list(continuous = my_dens))
ggpairs(vwx2, columns = 11:17,aes(color = classe),
lower = list(continuous = my_point),diag = list(continuous = my_dens))
abcF <- abc %>% select(final_cols,classe)
ghiF <- ghi %>% select(final_cols,classe)
abcF[,1:17] <- sapply(abcF[,1:17],as.numeric)
ghiF[,1:17] <- sapply(ghiF[,1:17],as.numeric)
levels <- c("A", "B", "C", "D", "E")
preprop_obj <- preProcess(abcF[,-18],method = c("center","scale","BoxCox"))
xTrain <- predict(preprop_obj,select(abcF,-classe))
yTrain <- factor(abcF$classe,levels=levels)
xVal <- predict(preprop_obj,select(ghiF,-classe))
yVal <- factor(ghiF$classe,levels=levels)
trControl <- trainControl(method="cv", number=5)
modelCT <- train(x = xTrain,y = yTrain,
method = "rpart", trControl = trControl)
modelRF <- train(x = xTrain,y = yTrain,
method = "rf", trControl = trControl,verbose=FALSE, metric = "Accuracy")
modelGBM <- train(x = xTrain,y = yTrain,
method = "gbm",trControl=trControl, verbose=FALSE)
modelSVM <- svm(x = xTrain,y = yTrain,
kernel = "polynomial", cost = 10)confusionMatrix(predict(modelCT,xVal),yVal)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1003 330 319 294 106
## B 19 256 20 109 103
## C 93 173 345 240 212
## D 0 0 0 0 0
## E 1 0 0 0 300
##
## Overall Statistics
##
## Accuracy : 0.4853
## 95% CI : (0.4696, 0.5011)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.3271
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.8987 0.33729 0.50439 0.0000 0.41609
## Specificity 0.6263 0.92067 0.77833 1.0000 0.99969
## Pos Pred Value 0.4888 0.50493 0.32455 NaN 0.99668
## Neg Pred Value 0.9396 0.85275 0.88147 0.8361 0.88377
## Prevalence 0.2845 0.19347 0.17436 0.1639 0.18379
## Detection Rate 0.2557 0.06526 0.08794 0.0000 0.07647
## Detection Prevalence 0.5231 0.12924 0.27097 0.0000 0.07673
## Balanced Accuracy 0.7625 0.62898 0.64136 0.5000 0.70789confusionMatrix(predict(modelRF,xVal),yVal)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1112 7 0 0 0
## B 3 741 5 3 1
## C 1 7 676 15 4
## D 0 4 3 625 1
## E 0 0 0 0 715
##
## Overall Statistics
##
## Accuracy : 0.9862
## 95% CI : (0.9821, 0.9896)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9826
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9964 0.9763 0.9883 0.9720 0.9917
## Specificity 0.9975 0.9962 0.9917 0.9976 1.0000
## Pos Pred Value 0.9937 0.9841 0.9616 0.9874 1.0000
## Neg Pred Value 0.9986 0.9943 0.9975 0.9945 0.9981
## Prevalence 0.2845 0.1935 0.1744 0.1639 0.1838
## Detection Rate 0.2835 0.1889 0.1723 0.1593 0.1823
## Detection Prevalence 0.2852 0.1919 0.1792 0.1614 0.1823
## Balanced Accuracy 0.9970 0.9862 0.9900 0.9848 0.9958plot(modelRF$finalModel,main="Error VS no of tree")
confusionMatrix(predict(modelGBM,xVal),yVal)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1083 40 2 5 3
## B 18 642 32 16 11
## C 8 54 635 39 10
## D 4 21 14 582 9
## E 3 2 1 1 688
##
## Overall Statistics
##
## Accuracy : 0.9253
## 95% CI : (0.9166, 0.9333)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9055
##
## Mcnemar's Test P-Value : 2.509e-07
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9704 0.8458 0.9284 0.9051 0.9542
## Specificity 0.9822 0.9757 0.9657 0.9854 0.9978
## Pos Pred Value 0.9559 0.8929 0.8512 0.9238 0.9899
## Neg Pred Value 0.9882 0.9635 0.9846 0.9815 0.9898
## Prevalence 0.2845 0.1935 0.1744 0.1639 0.1838
## Detection Rate 0.2761 0.1637 0.1619 0.1484 0.1754
## Detection Prevalence 0.2888 0.1833 0.1902 0.1606 0.1772
## Balanced Accuracy 0.9763 0.9108 0.9470 0.9452 0.9760confusionMatrix(predict(modelSVM,xVal),yVal)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1096 40 18 17 2
## B 1 676 15 5 6
## C 9 40 640 45 3
## D 10 3 9 575 9
## E 0 0 2 1 701
##
## Overall Statistics
##
## Accuracy : 0.9401
## 95% CI : (0.9322, 0.9473)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9241
##
## Mcnemar's Test P-Value : 1.808e-15
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9821 0.8906 0.9357 0.8942 0.9723
## Specificity 0.9726 0.9915 0.9701 0.9905 0.9991
## Pos Pred Value 0.9344 0.9616 0.8684 0.9488 0.9957
## Neg Pred Value 0.9927 0.9742 0.9862 0.9795 0.9938
## Prevalence 0.2845 0.1935 0.1744 0.1639 0.1838
## Detection Rate 0.2794 0.1723 0.1631 0.1466 0.1787
## Detection Prevalence 0.2990 0.1792 0.1879 0.1545 0.1795
## Balanced Accuracy 0.9773 0.9411 0.9529 0.9424 0.9857def2 <- def %>% select(final_cols,problem_id)
xTest <- def2 %>% select(final_cols)
result <- data.frame("problem_id" = def$problem_id,
"PREDICTION_RF" = predict(modelRF,xTest),
"PREDICTION_GBM" = predict(modelGBM,xTest),
"PREDICTION_SVM" = predict(modelSVM,xTest))
result## problem_id PREDICTION_RF PREDICTION_GBM PREDICTION_SVM
## 1 1 E E C
## 2 2 A E A
## 3 3 A D B
## 4 4 E E C
## 5 5 E E A
## 6 6 E D C
## 7 7 E E B
## 8 8 B D A
## 9 9 A B E
## 10 10 E E E
## 11 11 A E C
## 12 12 A D C
## 13 13 E B E
## 14 14 A D B
## 15 15 E E B
## 16 16 E E A
## 17 17 E E C
## 18 18 B E A
## 19 19 E E A
## 20 20 E E E